[R-SIG-Finance] Calculating Proportions & Appending New Column?

[arnaud gaboury]

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On Tue, Sep 23, 2014 at 2:37 AM, Ilya Kipnis <ilya.kipnis at gmail.com> wrote:
> Jason,
>
> This isn't a finance problem. This is the wrong list to post on. Does your
> class have a discussion forum?
>
> -Ilya
>
> On Mon, Sep 22, 2014 at 8:36 PM, Jason Eyerly <teamtraders3564 at gmail.com>
> wrote:
>
>> Hello Folks,
>>         I’m doing a study for a coursera Data Science class, and I am
>> trying to determine if American’s financial satisfaction has any
>> correlation to the percentage return of the S&P500 in the year prior. I
>> have to calculate the proportion of "Satisfied" and "More Or Less" compared
>> to the total number of observations for year "X", starting at 1989. After
>> computing that for each year, I need to place them in a column at the end
>> of the dataset similar to what we see with "PercentChange”. However, the
>> years only go from 1989 - 2012. Calculating it for each observation seems
>> tedious and inefficient. The end result is a chart where the X-Axis is each
>> different percent change, and the Y-Axis is the proportion that are
>> satisfied. What's the most efficient way to do this? Sorry for posting all
>> of my code, but I don’t know what’s important and what isn’t. I realize I
>> probably didn’t code everything in the most efficient way possible.
>>
>> require(Quandl)
>> require(lubridate)
>> require(zoo)
>> require(xts)
>>
>> myGSS <- load(url("http://bit.ly/dasi_gss_data"))
>>
>> year <- gss$year
>> finSat <- gss$satfin
>>
>> relativeTable <- data.frame(year, finSat)
>> relativeTable <- subset(relativeTable, year > "1988" & !is.na(finSat))
>>
>>
>> spReturns <- Quandl("SANDP/ANNRETS", trim_start="1970-01-11",
>>                     trim_end="2012-12-31", authcode="nwy3a_Gmd7TSS9fVirxT",
>>                     collapse="annual")
>>
>> percentChange <- spReturns$"Total Return Change"
>>
>> spReturns$"Year Ending" <- format((spReturns$"Year Ending"), "%Y")
>> spReturns$"Year Ending" <- as.numeric(spReturns$"Year Ending")
>> spReturns$"Year Ending" <- spReturns[,1] + 1 #the following year
>>
>> combined <- merge(relativeTable, spReturns, by.x = "year", by.y = "Year
>> Ending")
>> names(combined)[6] <- "percentChange"
>>
>> finalResults <- data.frame(combined$year, combined$finSat,
>> combined$percentChange)
>> names(finalResults)[1] <- "Year"
>> names(finalResults)[2] <- "FinancialSatisfaction"
>> names(finalResults)[3] <- "PercentChange"
>>
>> finalResults$PercentChange <- finalResults$PercentChange * 100
>>
>> Regards,
>>     Jason E.
>>         [[alternative HTML version deleted]]
>>
>>
>> _______________________________________________
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>> should go.
>>
>
>         [[alternative HTML version deleted]]
>
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